This disclosure relates generally to chemical analysis methods, and more particularly to the identification of chemical components in a mixture using nuclear magnetic resonance spectra.
Reliable analysis of complex mixtures plays a critical role in many areas of chemistry and the life sciences; however, the identification of components in chemical mixtures, such as different solutes in a solution, remains a fundamental problem in many areas of chemistry. The recent advent of metabolomics has generated a critical demand for powerful analysis methods of fluid mixtures for the food and life sciences. While important progress is being made in potentially laborious and costly hyphenation methods, spectroscopic methods have the power to circumvent or reduce the need for hyphenation prior to analysis. See Christophoridou, et al., J. Agric. Food Chem. 53, 4667-4679 (2005).
Most compounds contain multiple NMR active spins that are J-coupled, thereby allowing the identification of spin-spin coupling networks for the discrimination between components as well as their subsequent identification by screening against a database. Particularly useful in this regard is the 2D NMR 1H-1H TOCSY experiment, which monitors multiple relay transfers of spin magnetization within a spin system to provide a wealth of scalar spin-spin coupling connectivity information at a high sensitivity. Braunschweiler & Ernst, J. Magnetic Resonance 53, 521 (1983).
An unsupervised deconvolution method recently was proposed using principal component analysis (PCA) of the covariance TOCSY spectrum of a mixture. Zhang & Brüschweiler, Chemphyschem. 5, 794-796 (2004). In the absence of significant spectral overlap, the dominant PCA eigenmodes well approximate the 1D spectra of the individual components. However, increasing amounts of spectral overlaps between components results in “mixed modes” whose assignment to known compounds can pose a significant challenge. It would be desirable to provide a method to overcome these and other limitations.